Memfitting model AR(1)

Ingat bahwa Anda menggunakan pasangan ACF dan PACF untuk membantu mengidentifikasi orde \(p\) dan \(q\) pada model ARMA. Tabel berikut merangkum hasilnya:

	AR(\(p\))	MA(\(q\))	ARMA(\(p,q\))
ACF	Meluruh	Terpotong setelah lag \(q\)	Meluruh
PACF	Terpotong setelah lag \(p\)	Meluruh	Meluruh

Dalam latihan ini, Anda akan menghasilkan data dari model AR(1), $$X_t = .9 X_{t-1} + W_t,$$ mengamati data simulasi serta pasangan ACF dan PACF sampel untuk menentukan ordenya. Lalu, Anda akan memfitting model dan membandingkan parameter taksiran dengan parameter sebenarnya.

Sepanjang kursus ini, Anda akan menggunakan sarima() dari paket astsa untuk memfitting model ke data dengan mudah. Perintah tersebut menghasilkan grafik diagnostik residual yang dapat diabaikan sampai pembahasan diagnostik di bagian akhir bab.

Latihan ini adalah bagian dari kursus

Model ARIMA di R

Petunjuk latihan

Paket astsa sudah dimuat sebelumnya.
Gunakan perintah arima.sim() yang sudah disiapkan untuk menghasilkan 100 observasi dari model AR(1) dengan parameter AR 0,9. Simpan sebagai x.
Plot data yang dihasilkan menggunakan plot().
Plot pasangan ACF dan PACF sampel menggunakan perintah acf2() dari paket astsa.
Gunakan sarima() dari astsa untuk memfitting AR(1) ke data yang telah dihasilkan. Periksa t-table dan bandingkan estimasinya dengan nilai sebenarnya. Sebagai contoh, jika deret waktunya ada di x, untuk memfitting AR(1) ke data, gunakan sarima(x, p = 1, d = 0, q = 0) atau cukup sarima(x, 1, 0, 0).

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Generate 100 observations from the AR(1) model
x <- arima.sim(model = list(order = c(1, 0, 0), ar = .9), n = 100) 

# Plot the generated data 


# Plot the sample P/ACF pair


# Fit an AR(1) to the data and examine the t-table

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Model ARIMA di R

SkillTag.level.beginnerSkillTag.label

4.9+

Mulai Kursus Gratis

You will investigate the nature of time series data and learn the basics of ARMA models that can explain the behavior of such data. You will learn the basic R commands needed to help set up raw time series data to a form that can be analyzed using ARMA models.

Exercise 1: First things first Exercise 2: Data play Exercise 3: Elements of time series Exercise 4: Stationarity and nonstationarity Exercise 5: Differencing Exercise 6: Detrending data Exercise 7: Dealing with trend and heteroscedasticity Exercise 8: Stationary time series: ARMA Exercise 9: Simulating ARMA models

You will discover the wonderful world of ARMA models and how to fit these models to time series data. You will learn how to identify a model, how to choose the correct model, and how to verify a model once you fit it to data. You will learn how to use R time series commands from the stats and astsa packages.

Exercise 1: Model AR dan MA Exercise 2: Memfitting model AR(1)

Latihan Saat Ini

Exercise 3: Menyesuaikan model AR(2)Exercise 4: Menyesuaikan model MA(1)Exercise 5: AR dan MA bersama-sama Exercise 6: Memasang model ARMA Exercise 7: Identifikasi model ARMA Exercise 8: Pemilihan model dan analisis residual Exercise 9: Pemilihan model - I Exercise 10: Pemilihan model - II Exercise 11: Analisis residual - I Exercise 12: Analisis residual - II Exercise 13: Masuk ARMA

Now that you know how to fit ARMA models to stationary time series, you will learn about integrated ARMA (ARIMA) models for nonstationary time series. You will fit the models to real data using R time series commands from the stats and astsa packages.

Exercise 1: ARIMA - integrated ARMA Exercise 2: ARIMA - plug and play Exercise 3: Simulated ARIMA Exercise 4: Global warming Exercise 5: ARIMA diagnostics Exercise 6: Diagnostics - simulated overfitting Exercise 7: Diagnostics - global temperatures Exercise 8: Forecasting ARIMA Exercise 9: Forecasting simulated ARIMA Exercise 10: Forecasting global temperatures

You will learn how to fit and forecast seasonal time series data using seasonal ARIMA models. This is accomplished using what you learned in the previous chapters and by learning how to extend the R time series commands available in the stats and astsa packages.

Exercise 1: Pure seasonal models Exercise 2: P/ACF of pure seasonal models Exercise 3: Fit a pure seasonal model Exercise 4: Mixed seasonal models Exercise 5: Fit a mixed seasonal model Exercise 6: Data analysis - unemployment I Exercise 7: Data analysis - unemployment II Exercise 8: Data analysis - commodity prices Exercise 9: Data analysis - birth rate Exercise 10: Forecasting seasonal ARIMA Exercise 11: Forecasting monthly unemployment Exercise 12: How hard is it to forecast commodity prices?Exercise 13: Congratulations!